Loading packages

library(openxlsx)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.2.0     ✔ purrr   0.3.3
## ✔ tibble  2.1.3     ✔ dplyr   0.8.3
## ✔ tidyr   1.0.0     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(readxl)
library(ggplot2)

Import Data Page - New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/Page+Channel.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"                     "Default Channel Grouping"
##  [3] "Date Range"               "Segment"                 
##  [5] "Pageviews"                "Unique Pageviews"        
##  [7] "Avg. Time on Page"        "Entrances"               
##  [9] "Bounce Rate"              "% Exit"                  
## [11] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Default Channel Grouping"] <- "Channel" 
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Channel))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DA_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Channel- Page New Users

DA_20 <- DA_03 %>% select( Page, Channel, Pageviews, test_categ)
 
  
DA_24 <- DA_20 %>% select(Page, Channel, Pageviews, test_categ) %>% group_by(Channel, Page, test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, Channel, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 
 

DA_25$Channel<- as.factor(DA_25$Channel) 
DA_25 %>% filter(TotP > 150) %>% 
ggplot(aes(reorder(x=Page, TotP), y=TotP, fill=Channel)) + 
  geom_bar(stat="identity", position = "dodge")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 65, hjust = 1) ) + ggtitle("Fig. 1 Page Views by Channel by New Users") +xlab("Page") + ylab("TotalPage Views") +  scale_fill_manual(values=c("red", "darkgreen", "springgreen1", "turquoise2", "yellow",  "purple", "salmon2", "olivedrab3", "magenta2", "khaki3", "brown2", "maroon2", "coral1", "grey45", "goldenrod1", "slategray3","tan4", "orchid3", "hotpink4", "black", "brown4", "lightpink2" )) + guides(colour = guide_legend(override.aes = list(size=3, alpha = 1))) + scale_shape_manual(values=c(3:9)) + facet_wrap(~test_categ)

Import Data Page - Product-electronics New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/product+electronics.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPE_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product electronics- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
 
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 
 

 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  filter(TotP > 10) %>%
ggplot(aes(reorder(x=Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig. 2 Page Views for Electronic Products by New Users") +xlab("Page") + ylab("TotalPage Views")  + guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Product-Office New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/product+office.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPO_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product Office- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 
 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  
ggplot(aes(reorder(x=DA_25$Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig. 3 Page Views for Office Products by New Users") +xlab("Page") + ylab("TotalPage Views") +    guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Product-Drinkware New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/Product+drinkware.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPD_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product Drinkware- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 

 
 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  
ggplot(aes(reorder(x=DA_25$Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig. 4 Page Views for Drinkware Products by New Users") +xlab("Page") + ylab("TotalPage Views") +    guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Product-Bags New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/Product+Bags.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPBg_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product Bags- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 

 
 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  
ggplot(aes(reorder(x=DA_25$Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig 5 Page Views for Bag Products by New Users") +xlab("Page") + ylab("TotalPage Views") +    guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Product-lifestyle New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/product+lifestyle.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPL_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product -Lifestyle- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 

 
 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  
ggplot(aes(reorder(x=Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig 6 Page Views for Lifestyle Products by New Users") +xlab("Page") + ylab("TotalPage Views") +    guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Product-apparel New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/Product+apparel.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPA_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product -apparel- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 

 
 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  filter(TotP >100) %>%
ggplot(aes(reorder(x=Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig 7 Page Views for Apparel Products by New Users") +xlab("Page") + ylab("Total Page Views") +    guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Product-category not set New Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/product not set.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"              "Date Range"        "Segment"          
##  [4] "Pageviews"         "Unique Pageviews"  "Avg. Time on Page"
##  [7] "Entrances"         "Bounce Rate"       "% Exit"           
## [10] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
colnames(DA_01)[colnames(DA_01)=="Page Value"] <- "Page_Value"
colnames(DA_01)[colnames(DA_01)=="% Exit"] <- "percentage_Exit"
colnames(DA_01)[colnames(DA_01)=="Avg. Time on Page"] <- "Avg_Time_on_Page"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DPn_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Product -apparel- Page New Users

DA_20 <- DA_03 %>% select( Page, Pageviews, test_categ)
  
DA_24 <- DA_20 %>% select(Page, Pageviews, test_categ) %>% group_by(  Page,  test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 

 
 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>%  filter(TotP >100) %>%
ggplot(aes(reorder(x=Page, TotP), y=TotP )) + 
  geom_bar(stat="identity", fill= "coral1")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig 8 Page Views for Product categories not set by New Users") +xlab("Page") + ylab("Total Page Views") +    guides(colour = guide_legend(override.aes = list(size=3, alpha = 1)))   +facet_wrap(~test_categ)

Import Data Page - Brand Returning Users

DA <-  read_excel("~/Desktop/Job related/Surefoot/Surefoot 2/Surefoot- GA/Analysis - 2/test and control dates/New Users + Behaviour/Page+Brands.xlsx", sheet = 2)

Data wrangling

DA_01 <- DA 
names(DA)
##  [1] "Page"                           "Brands (Landing Content Group)"
##  [3] "Date Range"                     "Segment"                       
##  [5] "Pageviews"                      "Unique Pageviews"              
##  [7] "Avg. Time on Page"              "Entrances"                     
##  [9] "Bounce Rate"                    "% Exit"                        
## [11] "Page Value"
colnames(DA_01)[colnames(DA_01)=="Unique Pageviews"] <- "Unique_Pageviews"  
colnames(DA_01)[colnames(DA_01)=="Brands (Landing Content Group)"] <- "Brands" 
colnames(DA_01)[colnames(DA_01)=="Date Range"] <- "Date_Range" 
colnames(DA_01)[colnames(DA_01)=="Bounce Rate"] <- "Bounce_Rate"
  

DA_02 <- DA_01 %>% filter(!is.na(Page))

DA_03 <- DA_02 %>% mutate(test_categ = ifelse(Date_Range == "Oct 18, 2019 - Oct 31, 2019", "test-Oct 18,2019 - Oct 31,2019", "control-Oct 4,2019 - Oct 17,2019"))

DB_m <- DA_03 %>% filter(Date_Range == "Oct 18, 2019 - Oct 31, 2019")

Visualization Brands- Page Returning Users

DA_20 <- DA_03 %>% select( Page, Brands, Pageviews, test_categ)
 
  
DA_24 <- DA_20 %>% select(Page, Brands, Pageviews, test_categ) %>% group_by(Brands, Page, test_categ) %>% mutate(TotP = sum(Pageviews)) %>% select(Page, Brands, TotP, Pageviews, test_categ)
DA_25 <- unique(DA_24) 
 

DA_25$Brands<- as.factor(DA_25$Brands) 
DA_25$Page<- as.factor(DA_25$Page) 
DA_25 %>% filter(TotP > 150) %>% 
ggplot(aes(reorder(x=Page, TotP), y=TotP, fill=Brands)) + 
  geom_bar(stat="identity", position = "dodge")  + theme(plot.title = element_text(size =10),axis.text.x = element_text(size =7,angle = 75, hjust = 1) ) + ggtitle("Fig. 9 Page Views by Brands by Returning Users") +xlab("Page") + ylab("TotalPage Views") +  scale_fill_manual(values=c("red", "darkgreen", "springgreen1", "turquoise2", "yellow", "grey", "purple", "salmon2", "olivedrab3", "magenta2", "khaki3", "brown2", "maroon2", "coral1", "grey45", "goldenrod1", "slategray3","tan4", "orchid3", "hotpink4", "black", "brown4", "lightpink2" )) + guides(colour = guide_legend(override.aes = list(size=3, alpha = 1))) + scale_shape_manual(values=c(3:9)) +facet_wrap(~test_categ)

Linear Regression

model_01x <- lm(Pageviews ~ Page+Channel+Bounce_Rate+Entrances+Unique_Pageviews+percentage_Exit+Page_Value , data = DA_m)
 
a<- data.frame(summary(model_01x)$coef[summary(model_01x)$coef[,4] <= .05, 4])
colnames(a) <- c("p-value")
a
##                                                                 p-value
## Page/basket.html                                           4.621125e-11
## Page/google+redesign/accessories/quickview                 1.870733e-04
## Page/google+redesign/apparel/hats                          9.512652e-03
## Page/google+redesign/apparel/mens/mens+t+shirts/quickview  1.075808e-03
## Page/google+redesign/apparel/mens/quickview                2.758262e-25
## Page/google+redesign/apparel/womens/quickview              1.135691e-02
## Page/google+redesign/lifestyle/bags                        2.685857e-04
## Page/google+redesign/lifestyle/drinkware                   1.190044e-02
## Page/google+redesign/lifestyle/small+goods                 1.391307e-02
## Page/google+redesign/new                                   5.552926e-04
## Page/google+redesign/shop+by+brand/google                  9.464231e-03
## Page/google+redesign/shop+by+brand/google/quickview        8.751344e-03
## Page/google+redesign/shop+by+brand/youtube/quickview       1.136445e-02
## Page/home                                                  2.527697e-14
## Page/store.html                                            2.011060e-02
## Page/store.html/quickview                                  6.274067e-22
## Entrances                                                 6.290806e-128
## Unique_Pageviews                                           0.000000e+00